PROJECT SUMMARY Integrated analysis of coronary anatomy and biology using 18F-fluoride PET and CT angiography Each year, 735,000 Americans have an acute myocardial infarction (heart attack), and approximately 120,000 die from it. Heart attacks occur most commonly due to rupture of atherosclerotic plaques in coronary arteries. Despite this, current diagnostic and treatment algorithms make no allowance for the assessment of disease activity and currently all patients with atherosclerosis are treated in a similar manner. This failure to differentiate stable from active disease may result in potentially unnecessary or insufficient therapies. In a breakthrough series of studies, our co-investigators discovered that positron emission tomography (PET) with 18F-sodium- fluoride (18F-NaF; an inexpensive and widely available tracer approved by Food and Drug Administration) can readily identify plaque rupture and increased coronary plaque activity. We propose to build further on this success, by addressing several important remaining limitations that prevent us from translating this technology to broad clinical use. The limitations include complicated and subjective image analysis, underutilization of the concomitant coronary computed tomography angiography (CTA) for plaque characterization, inability to utilize prior CTA for the analysis of 18F-NaF PET, lack of methods to integrate all available PET and CTA data and significant motion of the coronaries during the PET scan. We propose a multi-faceted approach to automate and improve coronary 18F-NaF PET imaging by full integration with CTA and correction for cardiac, respiratory, and patient motion. The overall goal of the proposal is to optimize the measurement of disease activity in coronary atherosclerosis using integrated 18F-NaF PET/CTA imaging, with the opportunity to validate this development against clinical outcome in a ?real-world? multicenter patient study. For this work, we propose the following 3 specific aims: 1) to integrate quantification of CTA and PET image data 2) to develop new methods for simultaneous correction of cardiac, respiratory, and patient motion for coronary PET, and 3) to clinically evaluate new methods in a multicenter clinical trial (separately funded and already underway), further refining risk prediction for heart attacks with integrated PET+CTA risk score derived by machine learning. This work will lead to a robust and reproducible clinical method for stratification of patients for risk of heart attacks, with potential to be applied for the identification of patients who would most benefit from expensive, and potentially risky treatments. Our techniques could also be used in future clinical trials to test the efficacy of novel therapies. Moreover, the new analysis will be applicable to other PET tracers that may be developed to investigate other pathological processes in the coronary vasculature. The resulting software will be shared with clinical institutions performing coronary PET to facilitate standardization and automation of this novel plaque imaging technique.